In this paper, an expert multicriteria-based health assessment model using multilinear regression (MLR) is proposed. Transformers are in continuous operation, develop various stresses (thermal, electrical, etc.) affected deterioration of insulation and leads to failure. These failures can result in significant revenue losses. To mitigate this risk, continuous monitoring is essential. MLR is a prediction technique based on input and output parameters. However, for a large number of transformer diagnostic attributes, accuracy in predicting the health index (HI) is unreliable. To address this issue, a popular multicriteria approach is utilized to linearize the diagnostic data and reduce the input parameters according to IEEE standards. Data collected from 20 different transformers has been used for the present research. The present MCA-based MLA model is validated with root mean square error (RMSQ), correlational coefficients (R2) and compared with pre-known health condition and model proposed in ( T. Manoj and C. Ranga,: An intelligence expert system for quality health monitoring of power transformers using testing attributes. Engineering Research Express, 5(2), 025,033 (2023).). It has been proved that the present proposed model predicted the accuracy of 95%. It is beneficial for maintenance staff and diagnostic experts in understanding the current health condition of transformers.